%0 Journal Article %1 Yan2021-ci %A Yan, Yexin %A Stewart, Terrence C %A Choo, Xuan %A Vogginger, Bernhard %A Partzsch, Johannes %A Höppner, Sebastian %A Kelber, Florian %A Eliasmith, Chris %A Furber, Steve %A Mayr, Christian %D 2021 %I IOP Publishing %J Neuromorph. Comput. Eng. %K %N 1 %P 014002 %T Comparing Loihi with a SpiNNaker 2 prototype on low-latency keyword spotting and adaptive robotic control %V 1 %X Abstract We implemented two neural network based benchmark tasks on a prototype chip of the second-generation SpiNNaker (SpiNNaker 2) neuromorphic system: keyword spotting and adaptive robotic control. Keyword spotting is commonly used in smart speakers to listen for wake words, and adaptive control is used in robotic applications to adapt to unknown dynamics in an online fashion. We highlight the benefit of a multiply-accumulate (MAC) array in the SpiNNaker 2 prototype which is ordinarily used in rate-based machine learning networks when employed in a neuromorphic, spiking context. In addition, the same benchmark tasks have been implemented on the Loihi neuromorphic chip, giving a side-by-side comparison regarding power consumption and computation time. While Loihi shows better efficiency when less complicated vector-matrix multiplication is involved, with the MAC array, the SpiNNaker 2 prototype shows better efficiency when high dimensional vector-matrix multiplication is involved.

@article{Yan2021-ci, abstract = {Abstract We implemented two neural network based benchmark tasks on a prototype chip of the second-generation SpiNNaker (SpiNNaker 2) neuromorphic system: keyword spotting and adaptive robotic control. Keyword spotting is commonly used in smart speakers to listen for wake words, and adaptive control is used in robotic applications to adapt to unknown dynamics in an online fashion. We highlight the benefit of a multiply-accumulate (MAC) array in the SpiNNaker 2 prototype which is ordinarily used in rate-based machine learning networks when employed in a neuromorphic, spiking context. In addition, the same benchmark tasks have been implemented on the Loihi neuromorphic chip, giving a side-by-side comparison regarding power consumption and computation time. While Loihi shows better efficiency when less complicated vector-matrix multiplication is involved, with the MAC array, the SpiNNaker 2 prototype shows better efficiency when high dimensional vector-matrix multiplication is involved.}, added-at = {2024-09-10T11:56:37.000+0200}, author = {Yan, Yexin and Stewart, Terrence C and Choo, Xuan and Vogginger, Bernhard and Partzsch, Johannes and H{\"o}ppner, Sebastian and Kelber, Florian and Eliasmith, Chris and Furber, Steve and Mayr, Christian}, biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/2e6ced2e87d60899a569faf56aa0bd41f/scadsfct}, copyright = {https://creativecommons.org/licenses/by/4.0/}, interhash = {ed63e62677a75db37508419d61e8121b}, intrahash = {e6ced2e87d60899a569faf56aa0bd41f}, journal = {Neuromorph. Comput. Eng.}, keywords = {}, month = sep, number = 1, pages = 014002, publisher = {IOP Publishing}, timestamp = {2024-09-10T15:15:57.000+0200}, title = {Comparing Loihi with a {SpiNNaker} 2 prototype on low-latency keyword spotting and adaptive robotic control}, volume = 1, year = 2021 }